The story

Scarcity in the workforce isn’t new for Southwest Minnesota, but it’s severity is increasing. Demographic changes and economic growth are all contributing to the problem.

Due to this issue, leaders are looking to invest in programs and initiatives that will help grow the workforce. One of the primary areas is education.

It’s well recorded that since the 1970s, education has played a dominant role in training our workforce to meet the economic demands. However, there is also a lot of evidence showing that education has encouraged young people to leave rural areas to find greater economic opportunity. Leaders are now looking at education as a tool to grow their local, rural workforce and it’s role in promoting local opportunities.

Meaningful employment at each Time X

So, lets answer the main question, what percentage of graduates from Southwest Minnesota high schools have meaningful employment the year they graduate, two years after they graduate, and seven years after they graduate.

The chart below provides the percentage of graduates in Southwest Minnesota from 2008 to 2019 that had meaningful employment in the same county or EDR as their high school, within one of the 3 research EDRs, or in Minnesota. There are two trends to notice. First, is that the percentage increases as the number of years after graduating high school increases. Second, the percentages increase as the geographic location of measuring meaningful employment expands. This culminates when looking at the percentage of individuals from a Southwest Minnesota high school had meaningful employment in Minnesota seven years after graduation - 56.6% of them.

What economic development leaders across Southwest Minnesota want to know is what factors play a role in people having meaningful employment in their local geography.

The factors at Time X = grad year +0

The Classification and Regression Tree analysis concludes the following variables in our dataset as being important in predicting whether an individual has meaningful employment in the same regions as their high school the year of graduating high school.

1. High school accomplishments
   1. took.act
   2. cte.achievement
2. High school enrollment
   1. pseo.participant
3. Post-secondary pathway
   1. attended.ps.within.first.year.hsgrad
4. High school characteristics
   1. Dem_Desc
5. Demographic variables
   1. Race/Ethnicity

The charts below show the following;

A statistically significantly larger proportion of individuals with the following attributes had meaningful employment in the same geography as their high school;

• Did NOT take the ACT
• Took CTE courses (especially completors and concentrators)
• Did NOT take PSEO courses
• Did NOT attend post-secondary within the first year of graduating high school
• Graduated from a town/rural mix high school
• Were hispanic or black

Factors - high school accomplishments

Took ACT

A significantly larger proportion of individuals that did not take the ACT in high school had meaningful employment in the same county and EDR as their high school, or had meaningful employment in one of the three EDRS and/or Minnesota.

Achievement in Career and Technical Education

A significantly larger proportion of individual that were categorized as completors or concentrators in CTE programming had meaningful employment in the same county or EDR as their high school, or in one of the three EDRs and/or Minnesota. This was also true for individuals that participated in CTE courses but were not concentrators or completors.

Took ACT

County employment match

emp.match	took.ACT		Total
	No	Yes
Employment	2197 1565 17.4 %	2523 3155 9.9 %	4720 4720 12.4 %
No employment	10416 11048 82.6 %	22914 22282 90.1 %	33330 33330 87.6 %
Total	12613 12613 100 %	25437 25437 100 %	38050 38050 100 %
χ2=435.800 · df=1 · φ=0.107 · p=0.000

EDR employment match

emp.match	took.ACT		Total
	No	Yes
Employment	2650 1873 21 %	3001 3778 11.8 %	5651 5651 14.9 %
No employment	9963 10740 79 %	22436 21659 88.2 %	32399 32399 85.1 %
Total	12613 12613 100 %	25437 25437 100 %	38050 38050 100 %
χ2=565.143 · df=1 · φ=0.122 · p=0.000

Any three EDR employment match

emp.match	took.ACT		Total
	No	Yes
Employment	2917 2043 23.1 %	3246 4120 12.8 %	6163 6163 16.2 %
No employment	9696 10570 76.9 %	22191 21317 87.2 %	31887 31887 83.8 %
Total	12613 12613 100 %	25437 25437 100 %	38050 38050 100 %
χ2=666.742 · df=1 · φ=0.132 · p=0.000

MN employment match

emp.match	took.ACT		Total
	No	Yes
Employment	3505 2466 27.8 %	3934 4973 15.5 %	7439 7439 19.6 %
No employment	9108 10147 72.2 %	21503 20464 84.5 %	30611 30611 80.4 %
Total	12613 12613 100 %	25437 25437 100 %	38050 38050 100 %
χ2=813.338 · df=1 · φ=0.146 · p=0.000

Achievement in career and technology pathways

County employment match

emp.match	cte.achievement			Total
	CTE Concentrator or Completor	CTE Participant	No CTE
Employment	2630 2212 14.8 %	1304 1388 11.7 %	786 1120 8.7 %	4720 4720 12.4 %
No employment	15198 15616 85.2 %	9888 9804 88.3 %	8244 7910 91.3 %	33330 33330 87.6 %
Total	17828 17828 100 %	11192 11192 100 %	9030 9030 100 %	38050 38050 100 %
χ2=210.047 · df=2 · Cramer’s V=0.074 · p=0.000

EDR employment match

emp.match	cte.achievement			Total
	CTE Concentrator or Completor	CTE Participant	No CTE
Employment	3156 2648 17.7 %	1545 1662 13.8 %	950 1341 10.5 %	5651 5651 14.9 %
No employment	14672 15180 82.3 %	9647 9530 86.2 %	8080 7689 89.5 %	32399 32399 85.1 %
Total	17828 17828 100 %	11192 11192 100 %	9030 9030 100 %	38050 38050 100 %
χ2=258.234 · df=2 · Cramer’s V=0.082 · p=0.000

Any three EDR employment match

emp.match	cte.achievement			Total
	CTE Concentrator or Completor	CTE Participant	No CTE
Employment	3432 2888 19.3 %	1682 1813 15 %	1049 1463 11.6 %	6163 6163 16.2 %
No employment	14396 14940 80.7 %	9510 9379 85 %	7981 7567 88.4 %	31887 31887 83.8 %
Total	17828 17828 100 %	11192 11192 100 %	9030 9030 100 %	38050 38050 100 %
χ2=273.286 · df=2 · Cramer’s V=0.085 · p=0.000

MN employment match

emp.match	cte.achievement			Total
	CTE Concentrator or Completor	CTE Participant	No CTE
Employment	4123 3485 23.1 %	2019 2188 18 %	1297 1765 14.4 %	7439 7439 19.6 %
No employment	13705 14343 76.9 %	9173 9004 82 %	7733 7265 85.6 %	30611 30611 80.4 %
Total	17828 17828 100 %	11192 11192 100 %	9030 9030 100 %	38050 38050 100 %
χ2=315.678 · df=2 · Cramer’s V=0.091 · p=0.000

Factors - high school enrollment

Enrolled in PSEO

A significantly larger proportion of individuals that never enrolled in PSEO courses had meaningful employment the year they graduated high school.The percentage also increased for these individuals as the geography enlarged.

Enrolled in PSEO

County employment match

emp.match	pseo.participant		Total
	No	Yes
Employment	3608 3081 14.5 %	1112 1639 8.4 %	4720 4720 12.4 %
No employment	21233 21760 85.5 %	12097 11570 91.6 %	33330 33330 87.6 %
Total	24841 24841 100 %	13209 13209 100 %	38050 38050 100 %
χ2=295.316 · df=1 · φ=0.088 · p=0.000

EDR employment match

emp.match	pseo.participant		Total
	No	Yes
Employment	4332 3689 17.4 %	1319 1962 10 %	5651 5651 14.9 %
No employment	20509 21152 82.6 %	11890 11247 90 %	32399 32399 85.1 %
Total	24841 24841 100 %	13209 13209 100 %	38050 38050 100 %
χ2=378.231 · df=1 · φ=0.100 · p=0.000

Any three EDR employment match

emp.match	pseo.participant		Total
	No	Yes
Employment	4743 4024 19.1 %	1420 2139 10.8 %	6163 6163 16.2 %
No employment	20098 20817 80.9 %	11789 11070 89.2 %	31887 31887 83.8 %
Total	24841 24841 100 %	13209 13209 100 %	38050 38050 100 %
χ2=441.619 · df=1 · φ=0.108 · p=0.000

MN employment match

emp.match	pseo.participant		Total
	No	Yes
Employment	5700 4857 22.9 %	1739 2582 13.2 %	7439 7439 19.6 %
No employment	19141 19984 77.1 %	11470 10627 86.8 %	30611 30611 80.4 %
Total	24841 24841 100 %	13209 13209 100 %	38050 38050 100 %
χ2=523.870 · df=1 · φ=0.117 · p=0.000

Factors - post-secondary pathway

Attended post-secondary within first year after graduating high school

A significantly larger proportion of individuals that did not enroll in post-secondary within the first year of high school had meaningful employment the year they graduated high school.The percentage also increased for these individuals as the geography enlarged.

Attended post-secondary within first year after graduating high school

County employment match

emp.match	attended.ps.within.first.year.hsgrad		Total
	No	Yes
Employment	1803 1316 17 %	2917 3404 10.6 %	4720 4720 12.4 %
No employment	8802 9289 83 %	24528 24041 89.4 %	33330 33330 87.6 %
Total	10605 10605 100 %	27445 27445 100 %	38050 38050 100 %
χ2=285.320 · df=1 · φ=0.087 · p=0.000

EDR employment match

emp.match	attended.ps.within.first.year.hsgrad		Total
	No	Yes
Employment	2212 1575 20.9 %	3439 4076 12.5 %	5651 5651 14.9 %
No employment	8393 9030 79.1 %	24006 23369 87.5 %	32399 32399 85.1 %
Total	10605 10605 100 %	27445 27445 100 %	38050 38050 100 %
χ2=418.819 · df=1 · φ=0.105 · p=0.000

Any three EDR employment match

emp.match	attended.ps.within.first.year.hsgrad		Total
	No	Yes
Employment	2462 1718 23.2 %	3701 4445 13.5 %	6163 6163 16.2 %
No employment	8143 8887 76.8 %	23744 23000 86.5 %	31887 31887 83.8 %
Total	10605 10605 100 %	27445 27445 100 %	38050 38050 100 %
χ2=532.835 · df=1 · φ=0.118 · p=0.000

MN employment match

emp.match	attended.ps.within.first.year.hsgrad		Total
	No	Yes
Employment	2985 2073 28.1 %	4454 5366 16.2 %	7439 7439 19.6 %
No employment	7620 8532 71.9 %	22991 22079 83.8 %	30611 30611 80.4 %
Total	10605 10605 100 %	27445 27445 100 %	38050 38050 100 %
χ2=690.060 · df=1 · φ=0.135 · p=0.000

Factors - high school characteristics

High school county RUCA category

A significantly larger proportion of individuals that did not enroll in post-secondary within the first year of high school had meaningful employment the year they graduated high school.The percentage also increased for these individuals as the geography enlarged.

High school county RUCA category

County employment match

emp.match	Dem_Desc			Total
	Entirely rural	Town/rural mix	Urban/town/rural mix
Employment	476 684 8.6 %	3575 3294 13.5 %	669 742 11.2 %	4720 4720 12.4 %
No employment	5039 4831 91.4 %	22979 23260 86.5 %	5312 5239 88.8 %	33330 33330 87.6 %
Total	5515 5515 100 %	26554 26554 100 %	5981 5981 100 %	38050 38050 100 %
χ2=107.839 · df=2 · Cramer’s V=0.053 · p=0.000

EDR employment match

emp.match	Dem_Desc			Total
	Entirely rural	Town/rural mix	Urban/town/rural mix
Employment	676 819 12.3 %	4233 3944 15.9 %	742 888 12.4 %	5651 5651 14.9 %
No employment	4839 4696 87.7 %	22321 22610 84.1 %	5239 5093 87.6 %	32399 32399 85.1 %
Total	5515 5515 100 %	26554 26554 100 %	5981 5981 100 %	38050 38050 100 %
χ2=82.562 · df=2 · Cramer’s V=0.047 · p=0.000

Any three EDR employment match

emp.match	Dem_Desc			Total
	Entirely rural	Town/rural mix	Urban/town/rural mix
Employment	726 893 13.2 %	4675 4301 17.6 %	762 969 12.7 %	6163 6163 16.2 %
No employment	4789 4622 86.8 %	21879 22253 82.4 %	5219 5012 87.3 %	31887 31887 83.8 %
Total	5515 5515 100 %	26554 26554 100 %	5981 5981 100 %	38050 38050 100 %
χ2=128.840 · df=2 · Cramer’s V=0.058 · p=0.000

MN employment match

emp.match	Dem_Desc			Total
	Entirely rural	Town/rural mix	Urban/town/rural mix
Employment	841 1078 15.2 %	5552 5191 20.9 %	1046 1169 17.5 %	7439 7439 19.6 %
No employment	4674 4437 84.8 %	21002 21363 79.1 %	4935 4812 82.5 %	30611 30611 80.4 %
Total	5515 5515 100 %	26554 26554 100 %	5981 5981 100 %	38050 38050 100 %
χ2=112.162 · df=2 · Cramer’s V=0.054 · p=0.000

Factors - demographic

Race and Ethnicity

A significantly larger proportion of individuals that did not enroll in post-secondary within the first year of high school had meaningful employment the year they graduated high school.The percentage also increased for these individuals as the geography enlarged.

Race and ethnicity

County employment match

emp.match	RaceEthnicity					Total
	AI	Asian/PI	Black	Hispanic	White
Employment	52 49 13.1 %	132 132 12.4 %	124 93 16.6 %	549 340 20 %	3860 4102 11.7 %	4717 4717 12.4 %
No employment	346 349 86.9 %	934 934 87.6 %	623 654 83.4 %	2194 2403 80 %	29196 28954 88.3 %	33293 33293 87.6 %
Total	398 398 100 %	1066 1066 100 %	747 747 100 %	2743 2743 100 %	33056 33056 100 %	38010 38010 100 %
χ2=174.487 · df=4 · Cramer’s V=0.068 · p=0.000

EDR employment match

emp.match	RaceEthnicity					Total
	AI	Asian/PI	Black	Hispanic	White
Employment	55 59 13.8 %	168 158 15.8 %	134 111 17.9 %	623 408 22.7 %	4668 4912 14.1 %	5648 5648 14.9 %
No employment	343 339 86.2 %	898 908 84.2 %	613 636 82.1 %	2120 2335 77.3 %	28388 28144 85.9 %	32362 32362 85.1 %
Total	398 398 100 %	1066 1066 100 %	747 747 100 %	2743 2743 100 %	33056 33056 100 %	38010 38010 100 %
χ2=154.557 · df=4 · Cramer’s V=0.064 · p=0.000

Any three EDR employment match

emp.match	RaceEthnicity					Total
	AI	Asian/PI	Black	Hispanic	White
Employment	69 65 17.3 %	173 173 16.2 %	148 121 19.8 %	665 445 24.2 %	5105 5357 15.4 %	6160 6160 16.2 %
No employment	329 333 82.7 %	893 893 83.8 %	599 626 80.2 %	2078 2298 75.8 %	27951 27699 84.6 %	31850 31850 83.8 %
Total	398 398 100 %	1066 1066 100 %	747 747 100 %	2743 2743 100 %	33056 33056 100 %	38010 38010 100 %
χ2=152.173 · df=4 · Cramer’s V=0.063 · p=0.000

MN employment match

emp.match	RaceEthnicity					Total
	AI	Asian/PI	Black	Hispanic	White
Employment	79 78 19.8 %	224 209 21 %	186 146 24.9 %	762 537 27.8 %	6185 6467 18.7 %	7436 7436 19.6 %
No employment	319 320 80.2 %	842 857 79 %	561 601 75.1 %	1981 2206 72.2 %	26871 26589 81.3 %	30574 30574 80.4 %
Total	398 398 100 %	1066 1066 100 %	747 747 100 %	2743 2743 100 %	33056 33056 100 %	38010 38010 100 %
χ2=147.914 · df=4 · Cramer’s V=0.062 · p=0.000

The factors at Time X = grad year +2

The Classification and Regression Tree analysis concludes the following variables in our dataset as being important in predicting whether an individual has meaningful employment in the same regions as their high school the year of graduating high school.

1. ps.grad.InstitutionSector
2. avg.wages.pct.state
3. cte.achievement
4. ap.exam
5. edr
6. took.ACT
7. RaceEthnicity
8. pseo.participant
9. Dem_Desc
10. attended.ps.within.first.year.hsgrad

The following list includes these variables, as well as other variables (indicated with asterisk) that the cross-tab analysis indicated as having significant differences and helped explain variation.

1. Post-secondary pathway
   1. ps.grad.InstitutionSector
   2. highest cred level*
   3. ps.grad*
   4. attended.ps*
2. High school characteristics
   1. avg.wages.pct.mn
   2. EDR
   3. Dem_Desc
3. High school accomplishments
   1. cte.achievement
   2. took.act
   3. ap.exam
4. High school enrollment
   1. pseo.participant
   2. non-english home*
5. Demographic variables
   1. Race/Ethnicity*

The charts below show the following;

A statistically significantly larger proportion of individuals with the following attributes had meaningful employment in the same geography as their high school;

Factors - post-secondary pathway

Post-secondary institution sector graduated

A significantly larger proportion of individuals that graduated from a 2-year public college had meaningful employment in the same geography as their high school two years after graduation. The percentage also increased for these individuals as the geography enlarged.

It’s also worth mentioning that individuals that did not graduate or attend post-secondary had a larger proportion of individuals with meaningful employment, as well as individuals who graduated from multiple institution sectors.

Post-secondary institution sector graduated

County employment match

emp.match	ps.grad.InstitutionSector					Total
	Public, 2-year	Did not graduate or attend PS	Grad multiple sectors	All 4-year	Private and for-profit 2-year or less
Employment	1439 984 25.5 %	3488 2847 21.3 %	46 39 20.4 %	482 1243 6.8 %	82 423 3.4 %	5537 5537 17.4 %
No employment	4212 4667 74.5 %	12857 13498 78.7 %	179 186 79.6 %	6651 5890 93.2 %	2349 2008 96.6 %	26248 26248 82.6 %
Total	5651 5651 100 %	16345 16345 100 %	225 225 100 %	7133 7133 100 %	2431 2431 100 %	31785 31785 100 %
χ2=1327.407 · df=4 · Cramer’s V=0.204 · p=0.000

EDR employment match

emp.match	ps.grad.InstitutionSector					Total
	Public, 2-year	Did not graduate or attend PS	Grad multiple sectors	All 4-year	Private and for-profit 2-year or less
Employment	1914 1270 33.9 %	4461 3674 27.3 %	59 51 26.2 %	619 1603 8.7 %	92 546 3.8 %	7145 7145 22.5 %
No employment	3737 4381 66.1 %	11884 12671 72.7 %	166 174 73.8 %	6514 5530 91.3 %	2339 1885 96.2 %	24640 24640 77.5 %
Total	5651 5651 100 %	16345 16345 100 %	225 225 100 %	7133 7133 100 %	2431 2431 100 %	31785 31785 100 %
χ2=1907.131 · df=4 · Cramer’s V=0.245 · p=0.000

Any three EDR employment match

emp.match	ps.grad.InstitutionSector					Total
	Public, 2-year	Did not graduate or attend PS	Grad multiple sectors	All 4-year	Private and for-profit 2-year or less
Employment	2192 1434 38.8 %	5019 4148 30.7 %	62 57 27.6 %	700 1810 9.8 %	94 617 3.9 %	8067 8067 25.4 %
No employment	3459 4217 61.2 %	11326 12197 69.3 %	163 168 72.4 %	6433 5323 90.2 %	2337 1814 96.1 %	23718 23718 74.6 %
Total	5651 5651 100 %	16345 16345 100 %	225 225 100 %	7133 7133 100 %	2431 2431 100 %	31785 31785 100 %
χ2=2288.733 · df=4 · Cramer’s V=0.268 · p=0.000

MN employment match

emp.match	ps.grad.InstitutionSector					Total
	Public, 2-year	Did not graduate or attend PS	Grad multiple sectors	All 4-year	Private and for-profit 2-year or less
Employment	2868 1965 50.8 %	6743 5682 41.3 %	105 78 46.7 %	1160 2480 16.3 %	174 845 7.2 %	11050 11050 34.8 %
No employment	2783 3686 49.2 %	9602 10663 58.7 %	120 147 53.3 %	5973 4653 83.7 %	2257 1586 92.8 %	20735 20735 65.2 %
Total	5651 5651 100 %	16345 16345 100 %	225 225 100 %	7133 7133 100 %	2431 2431 100 %	31785 31785 100 %
χ2=2848.137 · df=4 · Cramer’s V=0.299 · p=0.000